With unprecedented and growing interest in data science education, there are limited educator materials that provide meaningful opportunities for learners to practice "statistical thinking," as defined by Wild and Pfannkuch, with messy data addressing real-world challenges. As a solution, Nolan and Speed advocated for bringing applications to the forefront in undergraduate statistics curriculum with the use of in-depth "case studies" to encourage and develop statistical thinking in the classroom. Limitations to this approach include the significant time investment required to develop a case study--namely, to select a motivating question and to create an illustrative data analysis--and the domain expertise needed. As a result, case studies based on realistic challenges, not toy examples, are scarce. To address this, we developed the Open Case Studies ("opencasestudies.org") project, which offers a new statistical and data science education case study model. This educational resource provides self-contained, multimodal, peer-reviewed, and open-source guides (or case studies) from real-world examples for active experiences of complete data analyses. We developed an educator's guide describing how to most effectively use the case studies, how to modify and adapt components of the case studies in the classroom, and how to contribute new case studies ("opencasestudies.org/OCS_Guide").